Open-set recognition (OSR) presents a critical challenge for Carrier-free Ultra-wideband (UWB) radar target detection, as conventional closed-set methods fail to identify unknown targets in complex environments. To address this, we propose a novel OSR framework integrating Variational Auto-Encoders (VAE) with Neural Spline Flows (NSF). Specifically, VAE is employed to extract low-dimensional latent representations of radar echoes, while NSF models the complex probability distributions within this latent space. We construct a composite scoring metric by fusing the normalized VAE reconstruction loss with NSF-derived probabilistic features. Furthermore, a dynamic thresholding strategy is introduced, which adapts to category statistics and sample sizes to collaboratively identify unknown samples, overcoming the limitations of fixed thresholds. Experimental results on Carrier-free UWB radar datasets demonstrate that the proposed method significantly outperforms state-of-the-art baselines across various openness levels and signal-to-noise ratios, exhibiting enhanced robustness and generalization.

Impulse radio ultrawideband (IR-UWB) radar has the advantages of low cost, high resolution, and independence of light and weather conditions. Its potential in human activity recognition (HAR) for IoT device sensing draws interest. One challenge in this domain is effectively representing spatial static and temporal dynamic information in echo sequences. Transformers, used extensively in NLP and CV, have powerful sequence long-range dependency modeling capabilities. However, in the field of radar HAR, the application research of transformers is still insufficient. In addition, there is currently a lack of publicly available IR-UWB radar human action data sets. To this end, we proposed various fine-grained feature image calculation methods and designed an IR-UWB Radar Human Activity data set (IURHA2023). This article presents a swin transformer encoder combining cosine similarity attention and patch overlap to obtain deep spatio-temporal features of human action feature images. Compared with other proposed transformer models or traditional CNNs and RNNs, the improved swin transformer encoder performs better. To further improve the feature learning capability of the backbone network and the robustness to echo variations, we propose a supervised contrastive learning-enhanced swin transformer (SCL-SwinT). It obtains distinctions and compact embeddings by comparing the similarities of positive and negative examples partitioned according to labels. Experimental results on the IURHA2023 data set show that SCL-SwinT achieves a recognition rate exceeding 90%, and the inference speed on IoT edge devices satisfies real-time applications. Ablation experiments demonstrate the effectiveness of the proposed components. In addition, SCL-SwinT exhibits good robustness to environmental factors like noise, multipath, and distance.

We propose an innovative vision transformer (ViT)-based architecture for synthetic aperture radar (SAR) automatic target recognition (ATR), which trains models in a self-supervised learning fashion. Compared with convolution neural networks (CNNs)-based models, transformer-based architectures further focus on locational information among features, enabling models to understand images globally. However, the integral challenge with transformer is that they commonly demand more samples for training than the CNN-based models. Furthermore, securing substantial labeled SAR images is typically a daunting task, particularly for noncooperative targets. To address these issues, the proposed model combines the ViT architecture with a contrastive learning framework. The process begins by pretraining the model using substantial unlabeled samples, followed by the execution of fine-tuning with limited labeled data. Besides, A data augmentation mechanism is designed for contrastive learning to enhance diversity and amounts of samples, simultaneously learning robust representations. Experiments conducted on MSTAR datasets demonstrate that the proposed model can perform very well on SAR image classification tasks even without sufficient labeled training samples.

In this study, we introduce MobileViTX, an enhanced MobileViT architecture for human activity recognition in impulse radio ultra-wideband (IR-UWB) radar applications. MobileViT is a lightweight Vision Transformer mainly consisting of MobileViT blocks and MobileNetv2 blocks. Modifications to the MobileNetv2 block include adding a Drop Path and an SE module and altering activation functions to hard-sigmoid and hard-swish. Additionally, the self-attention in the MobileViT block is transformed to possess linear complexity. These adjustments aim to accelerate inference while preserving high accuracy. We experiment with a dataset from 20 individuals performing 20 distinct actions, using 5-fold cross-validation to assess our model’s performance. Results show MobileViTX outperforms the original MobileViT and other models in both recognition rate and efficiency.

Suppressive interference is a common interference signal for synthetic aperture radar (SAR) that can seriously affect the target identification and imaging results of SAR. This paper proposes a method for suppressing suppressive jamming using blind source separation (BSS) for singular value and eigenvalue decomposition based on information entropy. First, we developed an airborne SAR imaging geometry model and a suppressive interference signal mixing model. Next, we perform blind signal separation of the interfered mixed signal by means of BSS based on singular value and eigenvalue decomposition. Then, we image the different signals we have extracted. Finally, we extract the features of the image domain for the separated signals and set the information entropy threshold by the difference of information entropy to identify the jamming signal and the source signal and obtain the source signal. This method uses eigenvalue and singular value decomposition for BSS and extracts the image domain features of the signal after BSS by information entropy and identifies the source signal by information entropy thresholding. This method compensates for the uncertainty in the decomposition of the signal by means of BSS. The signal loss is minimal and the similarity of the separated signal and the original signal is very high. Simulated and measured data demonstrate the feasibility of this algorithm.

The presence of blanket jamming, a typical form of airborne synthetic aperture radar (SAR) jamming, causes incoherent signals with strong power to enter the airborne SAR receiver, which significantly reduces the signal-to-noise ratio (SNR) of airborne SAR images and dramatically influences the imaging effect of airborne SAR. In this manuscript, an airborne SAR anti-blanket interference algorithm based on fractional order Fourier transform (FRFT) and second order blind identification (SOBI) is proposed. Firstly, a geometrical model for airborne SAR imaging under a blanket interference condition is developed. Then, a blind signal separation (BSS) algorithm using SOBI is presented. Finally, to solve the problem of separation uncertainty of the existing BSS algorithms, the FRFT is used for signal identification. This method uses the SOBI algorithm to perform the BSS algorithm. Considering that FRFT can detect linear frequency modulation (LFM) signals emitted by airborne SAR, FRFT is used to solve the disadvantages of the current BSS methods. This algorithm separates signals with a high degree of similarity and performs well in terms of blanket jamming suppression. Simulation and measured experiments prove the validity of the algorithm.

The carrier-free ultra-wideband (UWB) radar (impulse radar) has seen a recent surge of interest. In this letter, a novel recognition system for vehicles based on the carrier-free UWB radar is proposed, in which the sparse representation is introduced as an effective feature extraction method. Based on the original K-SVD algorithm, we provide a new dictionary learning (DL) idea. Instead of only embedding discrimination criteria in the objective function, we expand and improve the optimization procedure of the K-SVD algorithm. Moreover, to alleviate the impact of the signal diversity on the recognition performance, we propose a hierarchical code constraint (HCC) and bind it to the improved K-SVD model. In this way, signals from the same class but with different distributions will be represented by the corresponding dictionary atoms. Extensive experiments prove the improved K-SVD with an HCC-IKSVD can effectively take both reconstruction capability and discriminative power of the dictionary into consideration.

Synthetic aperture radar (SAR) may be attacked by multifarious kinds of the active-jamming, which will lead to the ineffectiveness of SAR in complex electromagnetic environment. In this paper, a novel multi-electromagnetic jamming counter-measure for the airborne SAR is proposed based on maximum signal-to-noise ratio (SNR) blind source separation. Firstly, the imaging geometry and multi-component mixed signal model of airborne SAR are established. Then, based on multi-component mixed signal matrix, a blind source separation (BSS) method based on the maximum SNR is proposed to separate the real target echo signal from the multi-electromagnetic jamming signals. After real target echo signal identification, the high resolution images of the interested target area can be achieved by the corresponding SAR imaging method. The simulated and measured data results are present to prove the feasibility and effectiveness of the proposed method.

Impulse radio ultrawideband (IR-UWB) radar has high range resolution, strong anti-jamming ability, and low power consumption and has been widely used in target detection and recognition. Currently, existing studies always extract artificial features of echo signals, such as time–frequency images, Doppler features, or time-domain features, and then distinguish these features through well-designed deep networks. However, these manual features are difficult to achieve task-invariant and disentangled representations. The target echo received by UWB radar also has amplitude, time-shift, and target-aspect sensitivity problems. To address the above problems, we propose a novel supervised contrastive learning (SupCon) framework to recognize different vehicles. Under label constraints, deep invariant representations are obtained through contrastive learning of echo signals, improving classification accuracy. First, a 1-D deep residual network (ResNet) is designed as the backbone, and the self-attention (SA) layer is added to extract long-range features of echo signals. Second, well-designed data augmentation methods can improve the performance of contrastive learning. Due to the integration of multiple data transformations, the model can learn invariant features by maximizing the mutual information between different signal transformations. Finally, we modify the SupCon loss function. It alleviates the conflict problem of simultaneously shrinking and expanding the distance between the positive samples in the feature space and improves the recognition performance of the model. Ablation experiments on the measured dataset show that the designed components of the method are effective. Comparative experiments on ultrawideband radar public datasets [Air Force Research Laboratory’s (AFRL) high-resolution range profile (HRRP), moving and stationary target acquisition and recognition (MSTAR)] also demonstrate the excellent classification performance of the proposed algorithm.

Linear array maneuvering synthetic aperture radar (SAR) can push the limitations of classical SAR on foresight 3-D imaging, which has captured the attentions of worldwide radar researchers for a long time. To reconstruct the position of foresight target, this paper puts forward a nonlinear chirp scaling method for 3-D foresight linear array maneuvering SAR (FLAM-SAR). According to the novel geometry configuration, a nonlinear chirp scaling operation is adopted to equalize the space-variant Doppler frequency modulation rate. The simulation results of the array point scatterers and real airplane target model are used to prove the validity of theoretical derivation and validate the 3-D imaging capacity of FLAM-SAR.

The rapid development of flight control technology has made unmanned aerial vehicles (UAVs) widely used in high-precision strikes on the battlefield. The premise of this is to achieve accurate target recognition using UAV-based radars. Aiming at three typical ground targets, including pedestrians, wheeled vehicles, and tracked vehicles, the micro-Doppler modulation caused by the random vibration of the UAV is analyzed in this article for the first time. To improve the recognition accuracy under low signal-to-noise ratios (SNRs), Doppler signals are transformed into time–frequency images, and a deep convolutional denoising encoder (DCDE) is designed to effectively remove the noise without suppressing micro-Doppler characteristics. To avoid the complicated micro-Doppler feature extraction, deep residual learning that can reduce the burden of network training and gain higher learning efficiency compared with traditional deep convolutional neural networks (DCNNs) is adopted. Recognition results under various occasions using denoised micro-Doppler images and designed residual learning network indicate that the proposed method has higher precision and better robustness than current methods. Even when the SNR is only −16 dB, the overall recognition accuracy still exceeds 90%.

As a promising technique, dictionary learning (DL) for target recognition has seen a recent surge in recent years. Although many methods have been proposed to obtain discriminative dictionaries or coefficients via incorporating various constraints into the objective function, there are still two issues. First, it is well known that kinds of discriminative criteria on the objective function often involve substantial optimized items, increasing computation cost. Second, noises in the real world inevitably degrade the classification performance, while most DL algorithms disregard that. Aiming at these two problems, a hierarchical DL model is proposed for vehicle recognition based on the carrier-free ultrawideband (UWB) radar. With the purpose of successfully determining the identity of targets, we first learn several class-specific subdictionaries. Then, considering that the actual environment is filled with noises, we divided the learned dictionary atoms into signal and disturbance atoms in accordance with sparse coefficients to establish the signal dictionary and noise dictionary, respectively. Finally, the clean data are recovered over the corresponding signal dictionary, and meanwhile, the classification task is achieved. This hierarchical DL method takes into account both the noise-robust ability and discriminative power of the learned dictionary, in which the “atom selection” mechanism dramatically speeds up calculations. What is more, rather than imposing discriminative restraints on the objective function, we improve the K-SVD-based optimization process to complete hierarchical DL. Experimental results on the measured and synthetic data corroborate the effectiveness of the proposed method even under low signal-to-noise ratio (SNR) values. Especially, to testify to the generalization ability of the proposed method, we evaluate our algorithm on a public synthetic aperture radar (SAR) dataset (MSTAR).

For forward-looking linear-array synthetic aperture radar (FLLA-SAR) with constant acceleration, the conventional hyperbolic range model is incorrect, and the range-dependent range cell migration (RCM) cannot be ignored any more. Hence, the traditional chirp scaling (CS) algorithm based on the hyperbolic range model also cannot be used. To overcome these problems, a modified range model for the FLLA-SAR which has taken the platform's acceleration into consideration is proposed in this letter. Based on the model, an improved CS method is proposed for FLLA-SAR to eliminate the space-variant characteristic of the RCM. Simulation results are presented to validate the range model and the proposed method.